1. Field
Embodiments of the present invention generally relate to biometric signatures and identification. More specifically, embodiments of the present invention describe a system and method for accurate biometric signatures and identification using projective invariant features of a subregion of the human body.
2. Description of the Related Art
Biometrics generally are methods of identifying a person based on a physiological characteristic. Among the features measured are: face, fingerprints, hand geometry, palmprints, iris, retinal, vein, and voice comparison. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions.
The first modern biometric device, called Identimat, was introduced on a commercial basis in 1976. It measured the shape of the hand and looked particularly at finger length. Shortly thereafter, fingerprint identification systems were widely used in law enforcement. Because of the rapid development of hardware, including computing processors and image capture devices, other biometric-based techniques began to thrive. As the technologies become more stable and trustworthy, biometric identification is expected to substitute the existing personal IDs, e.g. passports.
Fingerprint identification has been utilized as a positive human identifier for over 100 years, and is most widely used application of biometric technologies. However, it suffers from extracting some small unique features from the fingerprints of elderly people as well as manual laborers whose fingerprints are worn out. In addition, since fingerprint identification has been widely employed by law enforcement agencies, it becomes intrusive to individual's privacy. Furthermore, fingerprints acquisition generally require contact further increasing its intrusiveness.
For many applications, other less intrusive techniques that can serve a larger percentage of the population would be preferable. Alternative techniques that identify people based on unique geometric characteristics of subregions of the human body hold promise in addressing these concerns. One area where there has been much research is the hand geometry identification technique. As the name implies, it identifies a person by the geometric structure of hand. Hand geometry identification technique is based on the fact that nearly every person has reasonably different hand shape that does not change after certain age. The shape of hand is composed of certain measurements of the hand such as the length and the width of fingers, the thickness of fingers, the width of palm, the angle of some special points. In existing approaches, the shape of hand is looking at (Euclidean) geometric sizes of various hand features. Various methods are used to measure the size of the hand. These methods are most commonly based either on mechanical or optical principle. The latter ones are much more commonly used today. For instance, optical scanners and digital cameras are typical devices to capture the image of the hand. Constraining the hand allows measurements to be computed from these images and converted to actual sizes.
The biometric community has studied a variety of hand geometry techniques, with varying degrees of success. One approach uses a mirror to allow a side view, various features including widths of the fingers, lengths of the fingers, widths of the palm, as well as heights of the fingers and the palm to be measured. To provide for consistent positions of a hand to be measured, five pegs were used to guide the placement of user's hand on a flat surface of the imaging device.
A major limitation of the prior art is the need for contact, and often the need of pegs. This introduced several problems. First, pegs can deform the shape of the hand. Second, the dependence on alignment means improper placement of a hand can still happen due to the relatively complicated instruction, which can reduce the reliability of the system. Finally, the prior art requires contact, which can be objectionable as it requiring users to place hands where many strangers just put their hands.
Thus, existing biometrics, especially for hand geometry and palm or finger prints, or iris, generally require strongly controlled imaging, usually requiring direct contact or constrained very close proximity to the sensor. In addition, measurements used for the biometric signature described in many previous inventions are Euclidian metrics and hence are distorted under projection and can be effectively used only for identification under nearly identical sensing geometries.
All biometric identification systems seek to find features such the intra-subject variations of the feature measurements are small and do not significantly overlap inter-subject distribution of those features. Features that do not change at all can be called invariant features, e.g. the number of fingers on a subject's hand. More commonly biometric systems consider feature measurements that may vary slightly during measurement but such that over a range of measurement conditions, as constrained by the system design, the measurements vary only slightly. Hence these slightly varying features may be considered quasi-invariant. When considering if a measurement is invariant or quasi-invariant, one must specify what is the range of measurements conditions or transforms under which the invariant property is expected to hold. For example the number of fingers visible on the hand is invariant to general pose variations and movement of the hand, but only if all the fingers are seen. However, while the 3D length of a finger is a general quasi-invariant, the visible “length” of a finger in an image is strongly impacted by the viewing angle of that finger and the pose of that finger. Generally speaking the broader the class of transforms allowed, the fewer invariants that will exist, the less discriminating they will be and the more intra-subject variations can be expected, i.e. the quasi-invariant property holds only over a smaller region of the general transform space. At the other extreme some prior art systems uses “Euclidian invariant”, which restrict the sensing system so as preserve Euclidean distance between feature points, e.g. U.S. Pat. No. 5,956,671 teaches an approach to speech recognition that is shift invariant and hence ID Euclidian invariant. If the sensing constraints are not exactly met, then the measurements will vary producing only Euclidian quasi-invariant features. There has been limited work addressing features that are more general than Euclidian invariants but still formal invariants. U.S. Pat. No. 6,178,261 teach an approach to image-based recognition for extracting features that are scale and translation invariant, which is a subset of affine invariant features.
Considerable prior art exists for object recognition through affine invariants, i.e. properties that are invariant to changes in rotation, translation or scale. U.S. Pat. No. 6,362,875 describes a system with scale-invariant and rotation-invariant pattern recognition application that retrieves stored images representative of the object being viewed that includes data representative of a fiducial within the object image. For example U.S. Pat. No. 6,243,493 teaches an approach to a in writing recognition using features that are rotation, translation and scale invariant, i.e. affine invariant features defined as ratio of tangents, and a novel application of the normalized curvature feature. U.S. Pat. No. 6,694,054 introduces a pattern recognition process wherein the pattern characterization step is used to obtain [affine] invariant descriptors of the pattern with a Fourier-Mellin transform. A projective invariant, well known to those skilled in the art, is a property or measurement that is retained when an object is subject to perspective projection. It is stronger than a general invariant, potentially providing improved discrimination, but requires fewer assumptions than an affine or Euclidian invariant. A pure invariant would not change at all, more commonly people consider quasi-invariants where the feature chances very little over a wide range of projective angles.
A key issue when considering projective invariants for biometrics is the actual feature points used to derive the invariant. The features themselves must be stable and be able to be re-identified if true projective invariance is to be computed. A simple example is cross-ratios of feature points—if they cannot be consistently labeled then the cross-ratio cannot be “matched”. A subtler example is contour features such as the finger and hand outlines considered in some of the prior art. With just a minor rotation of the finger or hand the actual points on boundary are now either occluded, on the part that turned away, or are completely inside the contour. Thus the features themselves, the points on the occluding contour, are not viewpoint invariant and hence not suitable for use in defining projective invariants. For polygons however, which are the subject of the prior art in invariants, the discontinuity of the surface does make the boundary points stable and hence useful for computing projective invariants. Such features may be useful for defining Euclidean or 2D ridged transform invariant features.